TY  - BOOK
AU  - Unpingco, Jose
AU  - Unpingco, Jose
TI  - Python for probability, statistics, and machine learning
SN  - 978-3319307152
U1  - 005.133 UNP 
PY  - 2016///
CY  - USA
PB  - Springer
KW  - Probabilities--Data processing
KW  - Python (Computer program language)
KW  - Statistics--Data processing
KW  - Mathematical statistics
KW  - Data mining
N1  - Alpha/2434/ Rs.7399/-; 	Getting Started with Scientific Python --
Probability --
Statistics --
Machine Learning --
Notation
N2  - This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming. Explains how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods; Connects to key open-source Python communities and corresponding modules focused on the latest developments in this area; Outlines probability, statistics, and machine learning concepts using an intuitive visual approach, backed up with corresponding visualization codes
UR  - http://www.springer.com/in/book/9783319307152
ER  -